A Spark application developer wants to identify which operations cause shuffling, leading to a new...

Comprehensive and Detailed Explanation From Exact Extract:

Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.

From Spark documentation:

“groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key.”

Option A (groupBy + agg) → causes shuffle.

Options B, C, and D (filter, withColumn, select) → transformations that do not require shuffling; they are narrow dependencies.

Final Answer: A